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<p>This work proposes a straightforward and highly sensitive plasmonic sensor, especially for an analyte ranging from 1.33 to 142. To simplify the process, an external sensing approach is favored over internal sensing. As a result, a convex-shaped large analyte channel is designed on top of the fiber, facilitating easier handling of the sensing procedure, including filling and cleaning the channel. Besides that, chemically stable gold is preferred over other noble metals. Finite element method (FEM) simulation ensures that the proposed sensor can offer 24,000 nm/RIU of wavelength sensitivity (<i>W</i><sub>S</sub>) and 480 RIU<sup>-1</sup> of figure of merit (FOM). Additionally, Elastic Net is introduced to accurately predict refractive indices (RIs) by thoroughly analyzing simulation data. The model achieves a training mean square error (MSE) of approximately <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11468_2025_2781_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="82" /> </InlineMediaObject> <EquationSource Format="TEX">\(2.94\times 10_{-4}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>2.94</mn> <mo>×</mo> <msub> <mn>10</mn> <mrow> <mo>-</mo> <mn>4</mn> </mrow> </msub> </mrow> </math></EquationSource> </InlineEquation> and a prediction accuracy (<i>R</i><sup>2</sup>) of 0.9929, demonstrating its high predictive performance. With its remarkable sensitivity and precision, the proposed sensor shows promise as an effective tool for RI sensing. Furthermore, it holds potential applications in detecting pathogens in water and cancerous cells in humans.</p>

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Convex Analyte Channel Photonic Crystal Fiber Plasmonic Sensor and RI Prediction Incorporating Machine Learning Approach

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Basim Ahmad Alabsi,
  • Md. Aslam Mollah,
  • Abdulkarem H. M. Almawgani,
  • Afiquer Rahman,
  • Yahya Ali Abdelrahman Ali

摘要

This work proposes a straightforward and highly sensitive plasmonic sensor, especially for an analyte ranging from 1.33 to 142. To simplify the process, an external sensing approach is favored over internal sensing. As a result, a convex-shaped large analyte channel is designed on top of the fiber, facilitating easier handling of the sensing procedure, including filling and cleaning the channel. Besides that, chemically stable gold is preferred over other noble metals. Finite element method (FEM) simulation ensures that the proposed sensor can offer 24,000 nm/RIU of wavelength sensitivity (WS) and 480 RIU-1 of figure of merit (FOM). Additionally, Elastic Net is introduced to accurately predict refractive indices (RIs) by thoroughly analyzing simulation data. The model achieves a training mean square error (MSE) of approximately \(2.94\times 10_{-4}\) 2.94 × 10 - 4 and a prediction accuracy (R2) of 0.9929, demonstrating its high predictive performance. With its remarkable sensitivity and precision, the proposed sensor shows promise as an effective tool for RI sensing. Furthermore, it holds potential applications in detecting pathogens in water and cancerous cells in humans.